Overview

Dataset statistics

Number of variables20
Number of observations2310
Missing cells0
Missing cells (%)0.0%
Duplicate rows224
Duplicate rows (%)9.7%
Total size in memory361.1 KiB
Average record size in memory160.1 B

Variable types

NUM17
CAT3

Reproduction

Analysis started2020-08-25 01:50:34.186449
Analysis finished2020-08-25 01:51:20.091331
Duration45.9 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

region-pixel-count has constant value "9.0" Constant
Dataset has 224 (9.7%) duplicate rows Duplicates
rawred-mean is highly correlated with intensity-mean and 3 other fieldsHigh correlation
intensity-mean is highly correlated with rawred-mean and 3 other fieldsHigh correlation
rawblue-mean is highly correlated with intensity-mean and 3 other fieldsHigh correlation
rawgreen-mean is highly correlated with intensity-mean and 3 other fieldsHigh correlation
value-mean is highly correlated with intensity-mean and 3 other fieldsHigh correlation
vedge-mean has 48 (2.1%) zeros Zeros
vedge-sd has 49 (2.1%) zeros Zeros
hedge-mean has 48 (2.1%) zeros Zeros
hedge-sd has 49 (2.1%) zeros Zeros
intensity-mean has 47 (2.0%) zeros Zeros
rawred-mean has 138 (6.0%) zeros Zeros
rawblue-mean has 47 (2.0%) zeros Zeros
rawgreen-mean has 117 (5.1%) zeros Zeros
exred-mean has 50 (2.2%) zeros Zeros
exblue-mean has 48 (2.1%) zeros Zeros
exgreen-mean has 47 (2.0%) zeros Zeros
value-mean has 47 (2.0%) zeros Zeros
saturatoin-mean has 47 (2.0%) zeros Zeros
hue-mean has 47 (2.0%) zeros Zeros
target has 330 (14.3%) zeros Zeros

Variables

region-centroid-col
Real number (ℝ≥0)

Distinct count253
Unique (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.91385281385281
Minimum1.0
Maximum254.0
Zeros0
Zeros (%)0.0%
Memory size18.2 KiB
2020-08-25T01:51:20.136877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q162
median121
Q3189
95-th percentile239
Maximum254
Range253
Interquartile range (IQR)127

Descriptive statistics

Standard deviation72.95653249
Coefficient of variation (CV)0.5840547773
Kurtosis-1.21587097
Mean124.9138528
Median Absolute Deviation (MAD)63
Skewness0.05255950751
Sum288551
Variance5322.655633
2020-08-25T01:51:20.427514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
140200.9%
 
96200.9%
 
58200.9%
 
189180.8%
 
23180.8%
 
121170.7%
 
18170.7%
 
142160.7%
 
33160.7%
 
37160.7%
 
14160.7%
 
75150.6%
 
217150.6%
 
86150.6%
 
103150.6%
 
80140.6%
 
78140.6%
 
226140.6%
 
68140.6%
 
2140.6%
 
112140.6%
 
174140.6%
 
97140.6%
 
186140.6%
 
38130.6%
 
Other values (228)191783.0%
 
ValueCountFrequency (%) 
150.2%
 
2140.6%
 
340.2%
 
4120.5%
 
550.2%
 
6110.5%
 
790.4%
 
8100.4%
 
9110.5%
 
1090.4%
 
ValueCountFrequency (%) 
25440.2%
 
25350.2%
 
252130.6%
 
25140.2%
 
25080.3%
 
24970.3%
 
24870.3%
 
24770.3%
 
245120.5%
 
24490.4%
 

region-centroid-row
Real number (ℝ≥0)

Distinct count238
Unique (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.41731601731601
Minimum11.0
Maximum251.0
Zeros0
Zeros (%)0.0%
Memory size18.2 KiB
2020-08-25T01:51:20.538589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile30
Q181
median122
Q3172
95-th percentile220
Maximum251
Range240
Interquartile range (IQR)91

Descriptive statistics

Standard deviation57.48385092
Coefficient of variation (CV)0.4657681173
Kurtosis-0.7504723612
Mean123.417316
Median Absolute Deviation (MAD)42
Skewness0.1049833789
Sum285094
Variance3304.393117
2020-08-25T01:51:20.642801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
124291.3%
 
116251.1%
 
89241.0%
 
111241.0%
 
90231.0%
 
189231.0%
 
120221.0%
 
135221.0%
 
182221.0%
 
134221.0%
 
133210.9%
 
190200.9%
 
115200.9%
 
125200.9%
 
138190.8%
 
185190.8%
 
123190.8%
 
62190.8%
 
137190.8%
 
122190.8%
 
145180.8%
 
102180.8%
 
186170.7%
 
113170.7%
 
57170.7%
 
Other values (213)179277.6%
 
ValueCountFrequency (%) 
1130.1%
 
1250.2%
 
13100.4%
 
1430.1%
 
1590.4%
 
1640.2%
 
1770.3%
 
1890.4%
 
1970.3%
 
2080.3%
 
ValueCountFrequency (%) 
2511< 0.1%
 
25020.1%
 
24960.3%
 
24840.2%
 
24730.1%
 
24660.3%
 
24560.3%
 
24440.2%
 
24330.1%
 
24230.1%
 

region-pixel-count
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
9
2310
ValueCountFrequency (%) 
92310100.0%
 
2020-08-25T01:51:20.784834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
9231033.3%
 
.231033.3%
 
0231033.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number462066.7%
 
Other Punctuation231033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
9231050.0%
 
0231050.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2310100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6930100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
9231033.3%
 
.231033.3%
 
0231033.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6930100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
9231033.3%
 
.231033.3%
 
0231033.3%
 
Distinct count4
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
0
2032
0.11111111
 
259
0.22222222
 
18
0.33333334
 
1
ValueCountFrequency (%) 
0203288.0%
 
0.1111111125911.2%
 
0.22222222180.8%
 
0.333333341< 0.1%
 
2020-08-25T01:51:20.934029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.842424242
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0434248.9%
 
.231026.0%
 
1207223.3%
 
21441.6%
 
370.1%
 
41< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number656674.0%
 
Other Punctuation231026.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0434266.1%
 
1207231.6%
 
21442.2%
 
370.1%
 
41< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2310100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common8876100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0434248.9%
 
.231026.0%
 
1207223.3%
 
21441.6%
 
370.1%
 
41< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8876100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0434248.9%
 
.231026.0%
 
1207223.3%
 
21441.6%
 
370.1%
 
41< 0.1%
 
Distinct count3
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
0
2220
0.11111111
 
82
0.22222222
 
8
ValueCountFrequency (%) 
0222096.1%
 
0.11111111823.5%
 
0.2222222280.3%
 
2020-08-25T01:51:21.110020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.272727273
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0453059.9%
 
.231030.6%
 
16568.7%
 
2640.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number525069.4%
 
Other Punctuation231030.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0453086.3%
 
165612.5%
 
2641.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2310100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common7560100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0453059.9%
 
.231030.6%
 
16568.7%
 
2640.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7560100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0453059.9%
 
.231030.6%
 
16568.7%
 
2640.8%
 

vedge-mean
Real number (ℝ≥0)

ZEROS

Distinct count647
Unique (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.893939392176191
Minimum0.0
Maximum29.222221
Zeros48
Zeros (%)2.1%
Memory size18.2 KiB
2020-08-25T01:51:21.223550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22222222
Q10.7222215575
median1.2222239
Q32.166666925
95-th percentile4.697221475
Maximum29.222221
Range29.222221
Interquartile range (IQR)1.444445367

Descriptive statistics

Standard deviation2.698907966
Coefficient of variation (CV)1.425023407
Kurtosis39.05829654
Mean1.893939392
Median Absolute Deviation (MAD)0.6666654
Skewness5.554212332
Sum4374.999996
Variance7.284104208
2020-08-25T01:51:21.339089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0482.1%
 
0.6666667271.2%
 
0.5261.1%
 
1251.1%
 
0.8333333241.0%
 
0.44444442190.8%
 
1.6666666190.8%
 
0.5555555180.8%
 
0.33333334160.7%
 
1.1666666160.7%
 
2.277778160.7%
 
0.7777774140.6%
 
1.5140.6%
 
0.6111111140.6%
 
1.5555553140.6%
 
2.4444444140.6%
 
0.944444130.6%
 
0.72222227130.6%
 
0.88888884130.6%
 
0.94444466130.6%
 
1.888889130.6%
 
0.6111107130.6%
 
1.111111130.6%
 
0.7222226130.6%
 
1.722222130.6%
 
Other values (622)186980.9%
 
ValueCountFrequency (%) 
0482.1%
 
0.05555550420.1%
 
0.0555555521< 0.1%
 
0.05555555650.2%
 
0.05555556370.3%
 
0.0555555821< 0.1%
 
0.1111110840.2%
 
0.1111111041< 0.1%
 
0.1111111140.2%
 
0.1111111390.4%
 
ValueCountFrequency (%) 
29.2222211< 0.1%
 
27.9444431< 0.1%
 
27.2777791< 0.1%
 
25.520.1%
 
24.5555551< 0.1%
 
24.38889120.1%
 
24.333331< 0.1%
 
22.3888871< 0.1%
 
22.1666661< 0.1%
 
22.0555551< 0.1%
 

vedge-sd
Real number (ℝ≥0)

ZEROS

Distinct count1794
Unique (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.709319999043291
Minimum0.0
Maximum991.7184
Zeros49
Zeros (%)2.1%
Memory size18.2 KiB
2020-08-25T01:51:21.458032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06296294
Q10.3555547375
median0.83333297
Q31.8063675
95-th percentile7.54074
Maximum991.7184
Range991.7184
Interquartile range (IQR)1.450812763

Descriptive statistics

Standard deviation44.84645655
Coefficient of variation (CV)7.854955854
Kurtosis230.9562603
Mean5.709319999
Median Absolute Deviation (MAD)0.584911165
Skewness14.21761354
Sum13188.5292
Variance2011.204665
2020-08-25T01:51:21.557834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0492.1%
 
0.02962963780.3%
 
0.1721326170.3%
 
0.2407407260.3%
 
0.1333333460.3%
 
0.1518518560.3%
 
1.007407150.2%
 
0.3740741650.2%
 
0.329629550.2%
 
0.0629629350.2%
 
0.07777776650.2%
 
0.1222222350.2%
 
0.02962963150.2%
 
0.0629629440.2%
 
1.12962940.2%
 
0.6236095440.2%
 
0.1222221740.2%
 
0.0629629840.2%
 
0.11851849440.2%
 
1.143419340.2%
 
0.2518516540.2%
 
0.0629630140.2%
 
0.1360827740.2%
 
0.1360827840.2%
 
0.1721325540.2%
 
Other values (1769)214592.9%
 
ValueCountFrequency (%) 
0492.1%
 
0.018518131< 0.1%
 
0.0185184831< 0.1%
 
0.01851849620.1%
 
0.0185185111< 0.1%
 
0.0185185191< 0.1%
 
0.0185185220.1%
 
0.01851852430.1%
 
0.01851852820.1%
 
0.0185185351< 0.1%
 
ValueCountFrequency (%) 
991.71841< 0.1%
 
752.240661< 0.1%
 
749.762941< 0.1%
 
688.07411< 0.1%
 
572.996420.1%
 
530.55181< 0.1%
 
442.177861< 0.1%
 
375.096251< 0.1%
 
374.59641< 0.1%
 
356.166721< 0.1%
 

hedge-mean
Real number (ℝ≥0)

ZEROS

Distinct count702
Unique (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4247234040982684
Minimum0.0
Maximum44.722225
Zeros48
Zeros (%)2.1%
Memory size18.2 KiB
2020-08-25T01:51:21.663506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22222225
Q10.7777799
median1.4444441
Q32.5555556
95-th percentile7.97500025
Maximum44.722225
Range44.722225
Interquartile range (IQR)1.7777757

Descriptive statistics

Standard deviation3.610083744
Coefficient of variation (CV)1.488864148
Kurtosis40.57214906
Mean2.424723404
Median Absolute Deviation (MAD)0.7777774
Skewness5.319949089
Sum5601.111063
Variance13.03270464
2020-08-25T01:51:21.766048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0482.1%
 
0.8333333361.6%
 
0.6666667271.2%
 
1.3333334221.0%
 
1.8333334200.9%
 
1200.9%
 
1.1666666180.8%
 
2.1111114160.7%
 
2150.6%
 
1.6666666140.6%
 
1.111111140.6%
 
0.72222215140.6%
 
2.6666667130.6%
 
2.8333333130.6%
 
1.0555559130.6%
 
0.6111111130.6%
 
0.5130.6%
 
0.7777786130.6%
 
1.5130.6%
 
0.88888806130.6%
 
1.388889130.6%
 
0.4444445130.6%
 
0.33333334120.5%
 
0.7777774120.5%
 
2.222222120.5%
 
Other values (677)188081.4%
 
ValueCountFrequency (%) 
0482.1%
 
0.0555555041< 0.1%
 
0.0555555420.1%
 
0.055555556100.4%
 
0.0555555631< 0.1%
 
0.1111111190.4%
 
0.1111111390.4%
 
0.166666641< 0.1%
 
0.166666661< 0.1%
 
0.1666666740.2%
 
ValueCountFrequency (%) 
44.72222520.1%
 
43.3333321< 0.1%
 
34.611111< 0.1%
 
33.4444471< 0.1%
 
31.2222231< 0.1%
 
29.8888871< 0.1%
 
27.27777920.1%
 
26.61111320.1%
 
26.44444520.1%
 
25.2777791< 0.1%
 

hedge-sd
Real number (ℝ)

ZEROS

Distinct count1799
Unique (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.243692053240306
Minimum-1.5894573e-08
Maximum1386.3292
Zeros49
Zeros (%)2.1%
Memory size18.2 KiB
2020-08-25T01:51:21.874111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.5894573e-08
5-th percentile0.07407396575
Q10.421637325
median0.96296332
Q32.1832685
95-th percentile18.9690819
Maximum1386.3292
Range1386.3292
Interquartile range (IQR)1.761631175

Descriptive statistics

Standard deviation58.81151722
Coefficient of variation (CV)7.134123502
Kurtosis339.2208504
Mean8.243692053
Median Absolute Deviation (MAD)0.69079792
Skewness16.90092982
Sum19042.92864
Variance3458.794558
2020-08-25T01:51:21.977134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0492.1%
 
0.1360827790.4%
 
0.02962963770.3%
 
0.1721326170.3%
 
0.151851950.2%
 
0.0629629850.2%
 
0.07407407550.2%
 
0.02962963550.2%
 
0.2074073950.2%
 
0.272165550.2%
 
0.1333333450.2%
 
0.1518518640.2%
 
0.3442652240.2%
 
0.0888888740.2%
 
0.7123253340.2%
 
0.327730740.2%
 
0.3111111840.2%
 
0.444444440.2%
 
0.403686740.2%
 
0.9629628740.2%
 
0.4906534940.2%
 
0.07777776640.2%
 
0.1777777440.2%
 
0.5629628340.2%
 
0.429629540.2%
 
Other values (1774)214792.9%
 
ValueCountFrequency (%) 
-1.5894573e-081< 0.1%
 
0492.1%
 
0.0185184961< 0.1%
 
0.0185185111< 0.1%
 
0.0185185191< 0.1%
 
0.0185185220.1%
 
0.029629621< 0.1%
 
0.0296296241< 0.1%
 
0.0296296271< 0.1%
 
0.02962963130.1%
 
ValueCountFrequency (%) 
1386.329220.1%
 
1039.54081< 0.1%
 
889.36291< 0.1%
 
662.355471< 0.1%
 
650.429571< 0.1%
 
488.029751< 0.1%
 
409.396361< 0.1%
 
377.92951< 0.1%
 
338.640751< 0.1%
 
263.096341< 0.1%
 

intensity-mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count1271
Unique (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.051595338939826
Minimum0.0
Maximum143.44444
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:22.088915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7407407
Q17.296296
median21.592592
Q353.2129635
95-th percentile125.3000018
Maximum143.44444
Range143.44444
Interquartile range (IQR)45.9166675

Descriptive statistics

Standard deviation38.17640979
Coefficient of variation (CV)1.030358057
Kurtosis0.5672280977
Mean37.05159534
Median Absolute Deviation (MAD)18.0740736
Skewness1.274080793
Sum85589.18523
Variance1457.438265
2020-08-25T01:51:22.205899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0472.0%
 
1.1481482100.4%
 
0.7407407100.4%
 
0.703703780.3%
 
0.03703703780.3%
 
6.25925980.3%
 
1.370370480.3%
 
570.3%
 
16.18518470.3%
 
6.407407370.3%
 
15.3703770.3%
 
6.444444770.3%
 
1.407407470.3%
 
0.666666760.3%
 
0.555555660.3%
 
19.59259260.3%
 
0.777777860.3%
 
1.222222260.3%
 
1.518518660.3%
 
1.777777860.3%
 
6.666666560.3%
 
5.85185260.3%
 
5.88888960.3%
 
6.481481660.3%
 
0.518518560.3%
 
Other values (1246)209790.8%
 
ValueCountFrequency (%) 
0472.0%
 
0.03703703780.3%
 
0.07407407530.1%
 
0.1111111120.1%
 
0.1481481520.1%
 
0.185185181< 0.1%
 
0.2222222230.1%
 
0.2592592540.2%
 
0.296296330.1%
 
0.333333341< 0.1%
 
ValueCountFrequency (%) 
143.4444420.1%
 
141.814821< 0.1%
 
141.592591< 0.1%
 
141.555561< 0.1%
 
140.777771< 0.1%
 
140.407411< 0.1%
 
140.29631< 0.1%
 
140.259261< 0.1%
 
138.6296220.1%
 
138.0740820.1%
 

rawred-mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count681
Unique (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.82130841288312
Minimum0.0
Maximum137.11111
Zeros138
Zeros (%)6.0%
Memory size18.2 KiB
2020-08-25T01:51:22.323666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19.555555
Q347.333332
95-th percentile114.22222
Maximum137.11111
Range137.11111
Interquartile range (IQR)40.333332

Descriptive statistics

Standard deviation35.03677426
Coefficient of variation (CV)1.067500839
Kurtosis0.8102541531
Mean32.82130841
Median Absolute Deviation (MAD)17.33333385
Skewness1.33148208
Sum75817.22243
Variance1227.575551
2020-08-25T01:51:22.426341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01386.0%
 
0.11111111321.4%
 
1231.0%
 
7.5555553200.9%
 
0.5555556180.8%
 
0.22222222180.8%
 
0.33333334170.7%
 
0.44444445140.6%
 
7.4444447140.6%
 
6.5555553140.6%
 
4.111111130.6%
 
6.888889130.6%
 
1.1111112130.6%
 
7130.6%
 
7.3333335120.5%
 
7.111111120.5%
 
0.6666667120.5%
 
53.444443110.5%
 
0.8888889110.5%
 
7.6666665100.4%
 
6.7777777100.4%
 
7.7777777100.4%
 
11.666667100.4%
 
0.7777778100.4%
 
10.888889100.4%
 
Other values (656)183279.3%
 
ValueCountFrequency (%) 
01386.0%
 
0.11111111321.4%
 
0.22222222180.8%
 
0.33333334170.7%
 
0.44444445140.6%
 
0.5555556180.8%
 
0.6666667120.5%
 
0.7777778100.4%
 
0.8888889110.5%
 
1231.0%
 
ValueCountFrequency (%) 
137.111111< 0.1%
 
136.8888920.1%
 
135.111111< 0.1%
 
134.888891< 0.1%
 
134.666671< 0.1%
 
134.3333420.1%
 
133.888891< 0.1%
 
133.3333420.1%
 
1331< 0.1%
 
132.5555630.1%
 

rawblue-mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count781
Unique (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.187879274662336
Minimum0.0
Maximum150.88889
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:22.535677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8888888
Q19.555555
median27.666667
Q364.88889
95-th percentile140.727786
Maximum150.88889
Range150.88889
Interquartile range (IQR)55.333335

Descriptive statistics

Standard deviation43.52746112
Coefficient of variation (CV)0.9850543144
Kurtosis0.1124584103
Mean44.18787927
Median Absolute Deviation (MAD)21.555556
Skewness1.124643103
Sum102074.0011
Variance1894.639872
2020-08-25T01:51:22.654197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0472.0%
 
6.4444447170.7%
 
6.6666665150.6%
 
7.2222223150.6%
 
7.6666665130.6%
 
7.3333335130.6%
 
7.4444447120.5%
 
9110.5%
 
13.111111100.4%
 
5.6666665100.4%
 
6.5555553100.4%
 
3.8888888100.4%
 
6100.4%
 
6.7777777100.4%
 
6.222222390.4%
 
4.66666790.4%
 
48.2222290.4%
 
9.44444590.4%
 
1.222222290.4%
 
11.88888990.4%
 
6.88888990.4%
 
56.33333290.4%
 
28.77777990.4%
 
25.77777990.4%
 
890.4%
 
Other values (756)200886.9%
 
ValueCountFrequency (%) 
0472.0%
 
0.1111111180.3%
 
0.2222222230.1%
 
0.3333333420.1%
 
0.4444444530.1%
 
0.55555561< 0.1%
 
0.666666750.2%
 
0.777777840.2%
 
0.88888891< 0.1%
 
120.1%
 
ValueCountFrequency (%) 
150.8888920.1%
 
150.1111120.1%
 
149.8888920.1%
 
149.555561< 0.1%
 
149.111111< 0.1%
 
148.888891< 0.1%
 
147.8888920.1%
 
147.5555620.1%
 
147.444441< 0.1%
 
147.333341< 0.1%
 

rawgreen-mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count691
Unique (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.145599041406925
Minimum0.0
Maximum142.55556
Zeros117
Zeros (%)5.1%
Memory size18.2 KiB
2020-08-25T01:51:22.772289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.02777775
median20.333334
Q346.50000075
95-th percentile120.44444
Maximum142.55556
Range142.55556
Interquartile range (IQR)40.472223

Descriptive statistics

Standard deviation36.36477251
Coefficient of variation (CV)1.064991493
Kurtosis0.9210749011
Mean34.14559904
Median Absolute Deviation (MAD)17.4444452
Skewness1.379774491
Sum78876.33379
Variance1322.396679
2020-08-25T01:51:22.883830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01175.1%
 
0.11111111291.3%
 
3210.9%
 
3.5555556190.8%
 
0.33333334190.8%
 
3.3333333180.8%
 
3.6666667180.8%
 
0.6666667170.7%
 
15.888889160.7%
 
0.22222222150.6%
 
3.4444444150.6%
 
2.6666667140.6%
 
3.7777777140.6%
 
3.1111112140.6%
 
1130.6%
 
2.8888888130.6%
 
3.8888888120.5%
 
1.8888888120.5%
 
16.88889120.5%
 
15.666667120.5%
 
0.44444445120.5%
 
0.5555556120.5%
 
0.7777778110.5%
 
18.444445110.5%
 
3.2222223110.5%
 
Other values (666)183379.4%
 
ValueCountFrequency (%) 
01175.1%
 
0.11111111291.3%
 
0.22222222150.6%
 
0.33333334190.8%
 
0.44444445120.5%
 
0.5555556120.5%
 
0.6666667170.7%
 
0.7777778110.5%
 
0.888888990.4%
 
1130.6%
 
ValueCountFrequency (%) 
142.5555620.1%
 
1401< 0.1%
 
139.4444420.1%
 
137.888891< 0.1%
 
137.444441< 0.1%
 
137.333341< 0.1%
 
136.666671< 0.1%
 
135.333341< 0.1%
 
135.1111120.1%
 
13520.1%
 

exred-mean
Real number (ℝ)

ZEROS

Distinct count430
Unique (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.690860971424245
Minimum-49.666668
Maximum9.888889
Zeros50
Zeros (%)2.2%
Memory size18.2 KiB
2020-08-25T01:51:23.010096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-49.666668
5-th percentile-37.8388905
Q1-18.555555
median-10.888889
Q3-4.2222223
95-th percentile3.6666667
Maximum9.888889
Range59.555557
Interquartile range (IQR)14.3333327

Descriptive statistics

Standard deviation11.58356159
Coefficient of variation (CV)-0.9127482851
Kurtosis0.4923505354
Mean-12.69086097
Median Absolute Deviation (MAD)7.0000002
Skewness-0.8853609847
Sum-29315.88884
Variance134.1788991
2020-08-25T01:51:23.114307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0502.2%
 
-14.333333200.9%
 
-10.444445190.8%
 
-12190.8%
 
-9.222222170.7%
 
-1.4444444160.7%
 
-4.2222223160.7%
 
-9.888889160.7%
 
-11.888889160.7%
 
-6.888889150.6%
 
-10.888889150.6%
 
-4.6666665150.6%
 
-6.2222223140.6%
 
-8.777778140.6%
 
-13.555555140.6%
 
-5.111111140.6%
 
-8.888889140.6%
 
-16.444445140.6%
 
-1.5555556140.6%
 
-8.222222140.6%
 
-11.111111130.6%
 
-9130.6%
 
-13.888889130.6%
 
-2.3333333130.6%
 
-10.333333130.6%
 
Other values (405)189982.2%
 
ValueCountFrequency (%) 
-49.6666681< 0.1%
 
-49.4444431< 0.1%
 
-49.111111< 0.1%
 
-48.2222220.1%
 
-47.66666820.1%
 
-47.44444330.1%
 
-46.8888920.1%
 
-46.777781< 0.1%
 
-46.5555571< 0.1%
 
-46.3333321< 0.1%
 
ValueCountFrequency (%) 
9.8888891< 0.1%
 
7.22222231< 0.1%
 
6.77777771< 0.1%
 
6.33333351< 0.1%
 
6.22222231< 0.1%
 
61< 0.1%
 
5.88888930.1%
 
5.777777720.1%
 
5.666666540.2%
 
5.55555531< 0.1%
 

exblue-mean
Real number (ℝ)

ZEROS

Distinct count636
Unique (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.40885041904762
Minimum-12.444445
Maximum82.0
Zeros48
Zeros (%)2.1%
Memory size18.2 KiB
2020-08-25T01:51:23.224214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-12.444445
5-th percentile-6
Q14.138888825
median19.666666
Q335.77778
95-th percentile53.77778
Maximum82
Range94.444445
Interquartile range (IQR)31.63889118

Descriptive statistics

Standard deviation19.57181926
Coefficient of variation (CV)0.9141929099
Kurtosis-0.5186508927
Mean21.40885042
Median Absolute Deviation (MAD)15.666666
Skewness0.4201235152
Sum49454.44447
Variance383.0561092
2020-08-25T01:51:23.314729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0482.1%
 
3.1111112190.8%
 
3.3333333130.6%
 
25.444445130.6%
 
-4.888889120.5%
 
1.7777778110.5%
 
-3.4444444110.5%
 
19.666666110.5%
 
4.6666665100.4%
 
31.333334100.4%
 
27.666666100.4%
 
4.111111100.4%
 
0.22222222100.4%
 
23.88889100.4%
 
2.7777777100.4%
 
14.444445100.4%
 
2.6666667100.4%
 
12.666667100.4%
 
2.888888890.4%
 
10.33333390.4%
 
21.44444590.4%
 
2.444444490.4%
 
5.222222390.4%
 
25.8888990.4%
 
19.44444590.4%
 
Other values (611)200987.0%
 
ValueCountFrequency (%) 
-12.4444451< 0.1%
 
-12.1111111< 0.1%
 
-1220.1%
 
-11.66666720.1%
 
-11.44444520.1%
 
-11.11111130.1%
 
-1120.1%
 
-10.7777781< 0.1%
 
-10.33333320.1%
 
-10.22222230.1%
 
ValueCountFrequency (%) 
821< 0.1%
 
79.444441< 0.1%
 
79.2222220.1%
 
78.7777830.1%
 
78.1111151< 0.1%
 
77.6666641< 0.1%
 
77.5555620.1%
 
77.444441< 0.1%
 
76.8888851< 0.1%
 
75.555561< 0.1%
 

exgreen-mean
Real number (ℝ)

ZEROS

Distinct count377
Unique (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.71798940452381
Minimum-33.88889
Maximum24.666666
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:23.417477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-33.88889
5-th percentile-24.11111
Q1-16.777779
median-10.888889
Q3-3.2222223
95-th percentile15.555555
Maximum24.666666
Range58.555556
Interquartile range (IQR)13.5555567

Descriptive statistics

Standard deviation11.55162843
Coefficient of variation (CV)-1.325033548
Kurtosis0.04250924588
Mean-8.717989405
Median Absolute Deviation (MAD)6.777777
Skewness0.7814217002
Sum-20138.55552
Variance133.4401193
2020-08-25T01:51:23.703106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0472.0%
 
-7.6666665221.0%
 
-14.222222200.9%
 
-14.777778190.8%
 
-7.2222223180.8%
 
-13.444445180.8%
 
-15.777778180.8%
 
-15170.7%
 
-8160.7%
 
-14.444445160.7%
 
-13.666667160.7%
 
-12.333333160.7%
 
-1.7777778150.6%
 
-14.111111150.6%
 
-22.666666150.6%
 
-8.666667150.6%
 
-12.222222140.6%
 
-7140.6%
 
-17.222221140.6%
 
-7.4444447140.6%
 
-14140.6%
 
-7.5555553140.6%
 
-3.8888888130.6%
 
-17.555555130.6%
 
-8.111111130.6%
 
Other values (352)188481.6%
 
ValueCountFrequency (%) 
-33.8888920.1%
 
-32.888891< 0.1%
 
-32.3333321< 0.1%
 
-30.7777791< 0.1%
 
-30.55555550.2%
 
-30.3333341< 0.1%
 
-30.1111130.1%
 
-301< 0.1%
 
-29.7777791< 0.1%
 
-29.4444451< 0.1%
 
ValueCountFrequency (%) 
24.6666661< 0.1%
 
22.2222211< 0.1%
 
21.8888920.1%
 
21.6666661< 0.1%
 
21.2222211< 0.1%
 
20.66666630.1%
 
20.5555551< 0.1%
 
20.33333430.1%
 
2020.1%
 
19.888891< 0.1%
 

value-mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count785
Unique (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.137470414792205
Minimum0.0
Maximum150.88889
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:23.812379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8888888
Q111.555555
median28.666666
Q364.88889
95-th percentile140.727786
Maximum150.88889
Range150.88889
Interquartile range (IQR)53.333335

Descriptive statistics

Standard deviation42.92176362
Coefficient of variation (CV)0.95091203
Kurtosis0.1630799833
Mean45.13747041
Median Absolute Deviation (MAD)21.555555
Skewness1.134267904
Sum104267.5567
Variance1842.277792
2020-08-25T01:51:23.925223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0472.0%
 
7.6666665140.6%
 
7.7777777140.6%
 
7.2222223130.6%
 
7.5555553120.5%
 
8110.5%
 
6.888889110.5%
 
8.444445110.5%
 
3.8888888100.4%
 
23.88889100.4%
 
7.3333335100.4%
 
7.4444447100.4%
 
28.777779100.4%
 
48.2222290.4%
 
56.33333290.4%
 
1.222222290.4%
 
990.4%
 
7.88888990.4%
 
15.88888990.4%
 
7.11111180.3%
 
3.222222380.3%
 
3.555555680.3%
 
60.66666880.3%
 
2180.3%
 
2.888888880.3%
 
Other values (760)202587.7%
 
ValueCountFrequency (%) 
0472.0%
 
0.1111111180.3%
 
0.2222222230.1%
 
0.3333333420.1%
 
0.4444444530.1%
 
0.55555561< 0.1%
 
0.666666750.2%
 
0.777777840.2%
 
0.88888891< 0.1%
 
120.1%
 
ValueCountFrequency (%) 
150.8888920.1%
 
150.1111120.1%
 
149.8888920.1%
 
149.555561< 0.1%
 
149.111111< 0.1%
 
148.888891< 0.1%
 
147.8888920.1%
 
147.5555620.1%
 
147.444441< 0.1%
 
147.333341< 0.1%
 

saturatoin-mean
Real number (ℝ≥0)

ZEROS

Distinct count1899
Unique (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42689297609740257
Minimum0.0
Maximum1.0
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:24.066246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.152065382
Q10.2842348625
median0.37480313
Q30.54012346
95-th percentile0.9486904865
Maximum1
Range1
Interquartile range (IQR)0.2558885975

Descriptive statistics

Standard deviation0.2283094174
Coefficient of variation (CV)0.5348165235
Kurtosis0.4195104939
Mean0.4268929761
Median Absolute Deviation (MAD)0.108204425
Skewness0.9446758805
Sum986.1227748
Variance0.05212519006
2020-08-25T01:51:24.167153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1662.9%
 
0472.0%
 
0.1111111190.4%
 
0.977777890.4%
 
0.98412770.3%
 
0.986111170.3%
 
0.2222222260.3%
 
0.777777860.3%
 
0.888888960.3%
 
0.5641534350.2%
 
0.5385802450.2%
 
0.96296340.2%
 
0.581349240.2%
 
0.981481540.2%
 
0.555555640.2%
 
0.53703740.2%
 
0.5634920630.1%
 
0.545634930.1%
 
0.972222230.1%
 
0.2907884430.1%
 
0.2857561730.1%
 
0.543871230.1%
 
0.559171130.1%
 
0.3333333430.1%
 
0.5661375530.1%
 
Other values (1874)209090.5%
 
ValueCountFrequency (%) 
0472.0%
 
0.068883281< 0.1%
 
0.079805761< 0.1%
 
0.085016831< 0.1%
 
0.092592591< 0.1%
 
0.0927730120.1%
 
0.095183991< 0.1%
 
0.0962631620.1%
 
0.09635223420.1%
 
0.096560841< 0.1%
 
ValueCountFrequency (%) 
1662.9%
 
0.987654320.1%
 
0.986111170.3%
 
0.98412770.3%
 
0.981481540.2%
 
0.977777890.4%
 
0.9737654320.1%
 
0.972222230.1%
 
0.97023811< 0.1%
 
0.96913581< 0.1%
 

hue-mean
Real number (ℝ)

ZEROS

Distinct count1937
Unique (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3628969628348917
Minimum-3.0441751
Maximum2.9124804
Zeros47
Zeros (%)2.0%
Memory size18.2 KiB
2020-08-25T01:51:24.278271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-3.0441751
5-th percentile-2.38881642
Q1-2.188057075
median-2.05120005
Q3-1.5623083
95-th percentile2.3683105
Maximum2.9124804
Range5.9566555
Interquartile range (IQR)0.625748775

Descriptive statistics

Standard deviation1.545335443
Coefficient of variation (CV)-1.133860802
Kurtosis1.540804212
Mean-1.362896963
Median Absolute Deviation (MAD)0.21397445
Skewness1.782163506
Sum-3148.291984
Variance2.388061632
2020-08-25T01:51:24.385868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-2.0943952733.2%
 
0472.0%
 
-2.071241660.3%
 
-2.13309550.2%
 
-2.12325450.2%
 
-2.048717340.2%
 
-2.013143540.2%
 
-1.861154130.1%
 
-2.237561730.1%
 
-2.075070630.1%
 
-2.13263530.1%
 
-2.001124630.1%
 
-2.056155230.1%
 
-2.123904730.1%
 
-1.987598930.1%
 
-2.106234820.1%
 
-1.802554220.1%
 
-2.144507420.1%
 
-2.377965220.1%
 
-2.052258320.1%
 
-2.220553220.1%
 
-2.444562720.1%
 
-2.434692420.1%
 
-2.1118420.1%
 
-2.097814820.1%
 
Other values (1912)212291.9%
 
ValueCountFrequency (%) 
-3.04417511< 0.1%
 
-3.0150811< 0.1%
 
-2.8699481< 0.1%
 
-2.63938931< 0.1%
 
-2.61865071< 0.1%
 
-2.5938251< 0.1%
 
-2.58911091< 0.1%
 
-2.5872281< 0.1%
 
-2.57363491< 0.1%
 
-2.57338121< 0.1%
 
ValueCountFrequency (%) 
2.91248041< 0.1%
 
2.875121< 0.1%
 
2.864930620.1%
 
2.84341961< 0.1%
 
2.8356141< 0.1%
 
2.83482171< 0.1%
 
2.82710551< 0.1%
 
2.79167441< 0.1%
 
2.789800220.1%
 
2.7690381< 0.1%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0
Minimum0
Maximum6
Zeros330
Zeros (%)14.3%
Memory size18.2 KiB
2020-08-25T01:51:24.503019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.000433041
Coefficient of variation (CV)0.6668110137
Kurtosis-1.250107943
Mean3
Median Absolute Deviation (MAD)2
Skewness0
Sum6930
Variance4.001732352
2020-08-25T01:51:24.616663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
533014.3%
 
333014.3%
 
133014.3%
 
633014.3%
 
433014.3%
 
233014.3%
 
033014.3%
 
ValueCountFrequency (%) 
033014.3%
 
133014.3%
 
233014.3%
 
333014.3%
 
433014.3%
 
533014.3%
 
633014.3%
 
ValueCountFrequency (%) 
633014.3%
 
533014.3%
 
433014.3%
 
333014.3%
 
233014.3%
 
133014.3%
 
033014.3%
 

Interactions

2020-08-25T01:50:35.516623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:35.656610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:35.794800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:35.936154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.073158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.206254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.352101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.495207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.637662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.780145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:36.921125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.259366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.398257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.537158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.686415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.825095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:37.966792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.105821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.242615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.382249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.521329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.663692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.806893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:38.951719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.097709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.243365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.391320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.539879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.683420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.820440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:39.953936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.096693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.233552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.372942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.511074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.654470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.796542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:40.937385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:41.076404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:41.209337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:41.355120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:41.501551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:41.647951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.005663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.153854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.297549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.433818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.576229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.727765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:42.872318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.018621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.161493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.298496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.435753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.574023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.707529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:43.842394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.000327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.144534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.286017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.425651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.565375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.699192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.828437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:44.962235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.103613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.242455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.379980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.517533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.649612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.789551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:45.923606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.054372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.181377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.319724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.662534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.805869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:46.946920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.086689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.222314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.351212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.487818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.626201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.765470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:47.900024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.035471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.180784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.332565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.488192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.634794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.780332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:48.934377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.093987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.246928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.402842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.564407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.712173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.851050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:49.999968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.151783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.298521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.458975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.608807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.756807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:50.905462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.058989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.391006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.534382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.687156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.838763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:51.992829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.144338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.296236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.446028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.589256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.736890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:52.891603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.040223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.188009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.344905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.497954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.645556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.803417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:53.954215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.107322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.272469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.446202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.601126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.752718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:54.907417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.065704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.206889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.353708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.509924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.665598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.824870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:55.969817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:56.328603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:56.478084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:56.627775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:56.772761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:56.914739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.069231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.222426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.374225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.531344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.730241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:57.931343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.080491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.232309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.383457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.529183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.685038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.834335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:58.982409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:59.134787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:59.283121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:59.427433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:50:59.731889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:50:59.895967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.057742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.220290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.380291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.533353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.675030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:00.821876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:51:01.326556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:51:02.196719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:02.333622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:51:02.633171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:02.784711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:02.935711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.088135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.242195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.383605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.528906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.677645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.823926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:03.982940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.135833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.273570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.408800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.540666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.670979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.801717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:04.943445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.083378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.221724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.362695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.500577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.631994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:05.759179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.097004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.236957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.371956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.512918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.647326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.790163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:06.926994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.075160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.212093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.346935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.493008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.637772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.780930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:07.923508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.067575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.208659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.340014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.472054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.609444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.751806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:08.891075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.029452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.174639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.322693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.471969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.613351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.753331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:09.905866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:10.056643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:10.207752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:10.362438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:10.517048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:10.872712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.011385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.154696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.305962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.454154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.609463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.756028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:11.902049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.045440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.187578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.324013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.460727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.609751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.753928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:12.897600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.046476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.191978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.334749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.469557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.604945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.749530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:13.890687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.043035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.181956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.323550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.466101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.608255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.746471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:14.879738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.028740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.173326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.318268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.665077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.810734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:15.954678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.090682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.228995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.372158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.517947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.658273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.801099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:16.941272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.080134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.222716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.362044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.497379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.641903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.787380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:17.932140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.087197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.238066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.379141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.516116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.655990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.801330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:18.942463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:19.088107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:51:24.768189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:51:25.135143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:51:25.496108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:51:25.859215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:51:26.153522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:51:19.384212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:51:19.891534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

region-centroid-colregion-centroid-rowregion-pixel-countshort-line-density-5short-line-density-2vedge-meanvedge-sdhedge-meanhedge-sdintensity-meanrawred-meanrawblue-meanrawgreen-meanexred-meanexblue-meanexgreen-meanvalue-meansaturatoin-meanhue-meantarget
0140.0125.09.00.0000000.00.2777780.0629630.6666670.3111116.1851857.3333347.6666663.5555563.4444444.444445-7.8888897.7777780.545635-1.1218180
1188.0133.09.00.0000000.00.3333330.2666670.5000000.0777786.6666668.3333347.7777783.8888895.0000003.333333-8.3333338.4444450.538580-0.9248170
2105.0139.09.00.0000000.00.2777780.1074070.8333330.5222226.1111117.5555557.2222223.5555564.3333343.333333-7.6666667.5555550.532628-0.9659460
334.0137.09.00.0000000.00.5000000.1666671.1111110.4740745.8518527.7777786.4444453.3333335.7777781.777778-7.5555557.7777780.573633-0.7442720
439.0111.09.00.0000000.00.7222220.3740740.8888890.4296296.0370377.0000007.6666663.4444442.8888894.888889-7.7777787.8888890.562919-1.1757730
516.0128.09.00.0000000.00.5000000.0777780.6666670.3111115.5555556.8888896.6666663.1111114.0000003.333333-7.3333347.1111110.561508-0.9858110
626.067.09.00.1111110.01.0000000.8888902.4444453.18518520.00000019.55555525.88889014.555555-1.33333317.666666-16.33333425.8888900.436939-1.6232020
714.0110.09.00.0000000.01.7222225.3518502.6666671.02222317.92592618.88889021.44444513.4444452.88888910.555555-13.44444521.4444450.368848-1.3450960
811.0108.09.00.0000000.01.3333330.8000001.3888890.95185217.66666619.00000021.11111012.8888894.00000010.333333-14.33333321.1111100.388756-1.3021330
985.0101.09.00.0000000.01.3333331.2888881.2777781.21851821.29629721.22222126.77777915.888889-0.22222216.444445-16.22222126.7777790.404792-1.5585990

Last rows

region-centroid-colregion-centroid-rowregion-pixel-countshort-line-density-5short-line-density-2vedge-meanvedge-sdhedge-meanhedge-sdintensity-meanrawred-meanrawblue-meanrawgreen-meanexred-meanexblue-meanexgreen-meanvalue-meansaturatoin-meanhue-meantarget
2300152.0155.09.00.0000000.00.5000000.61111110.777778131.8073907.2962965.33333411.0000005.555555-5.88888911.111111-5.22222211.0000000.500000-2.1155676
230179.0154.09.00.0000000.01.1111111.5851850.1666670.0777780.7407410.4444441.5555560.222222-0.8888892.444444-1.5555561.5555560.288889-1.9196396
2302166.0154.09.00.0000000.00.1111110.0296300.4444440.0740740.7037040.2222221.8888890.000000-1.4444443.555556-2.1111111.8888891.000000-2.0175106
2303173.0158.09.00.1111110.01.8888893.6740741.1666670.4777788.3703707.11111111.7777786.222222-3.77777810.222222-6.44444511.7777780.472975-1.9046511
2304233.0159.09.00.0000000.02.5000000.5222221.2777781.5740746.6296305.11111110.1111114.666667-4.55555510.444445-5.88888910.1111110.541667-1.9994431
230532.0158.09.00.0000000.00.9444450.8629630.8333330.6111117.9629636.33333411.8888895.666666-4.88888911.777778-6.88888911.8888890.520578-1.9828341
23068.0162.09.00.1111110.01.6111112.0629620.3333330.1333338.3703706.66666612.0000006.444445-5.11111110.888889-5.77777812.0000000.484805-2.0449461
2307128.0161.09.00.0000000.00.5555550.2518520.7777780.1629637.1481485.55555510.8888895.000000-4.77777811.222222-6.44444510.8888890.540918-1.9963071
2308150.0158.09.00.0000000.02.1666671.6333341.3888890.4185188.4444457.00000012.2222226.111111-4.33333411.333333-7.00000012.2222220.503086-1.9434491
2309124.0162.09.00.1111110.01.3888891.1296302.0000000.88888910.0370378.00000014.5555557.555555-6.11111113.555555-7.44444514.5555550.479931-2.0293121

Duplicate rows

Most frequent

region-centroid-colregion-centroid-rowregion-pixel-countshort-line-density-5short-line-density-2vedge-meanvedge-sdhedge-meanhedge-sdintensity-meanrawred-meanrawblue-meanrawgreen-meanexred-meanexblue-meanexgreen-meanvalue-meansaturatoin-meanhue-meantargetcount
117137.0182.09.00.00.0000001.8333333.8555553.6111119.21852234.92592631.44444542.88889030.444445-10.44444523.888890-13.44444542.8888900.285756-2.00112543
179205.0190.09.00.00.0000001.2777770.9981451.6111111.12381649.48148044.77778060.66666843.000000-14.11111133.555557-19.44444560.6666680.290788-1.98759943
01.081.09.00.00.00000012.166667267.4555409.222222205.36296021.33333414.00000030.55555519.444445-22.00000027.666666-5.66666630.5555550.595282-2.43840922
12.044.09.00.00.0000002.1666672.3888882.3888901.52962918.74074017.33333425.22222113.666667-4.22222219.444445-15.22222225.2222210.457681-1.75372602
22.0245.09.00.00.0000001.8888892.1629633.1666673.2777786.4074076.2222226.0000007.000000-0.555556-1.2222221.7777787.2222220.1910491.75664532
34.0189.09.00.00.0000002.0555563.88518511.722221114.59634026.44444523.44444533.00000022.888890-9.00000019.666666-10.66666733.0000000.271473-2.10100242
44.0201.09.00.00.2222222.7222221.6788465.2222213.12457453.70370547.11111067.22222046.777780-19.77777940.555557-20.77777967.2222200.308859-2.07522642
55.0210.09.00.00.1111112.1666671.6699984.4444442.61335651.29629545.44444364.33333644.111110-17.55555539.111110-21.55555564.3333360.317566-2.02089542
66.081.09.00.00.1111114.1111118.7407455.72222328.50741012.4814827.66666618.88889010.888889-14.44444519.222221-4.77777818.8888900.628156-2.38856122
77.018.09.00.00.0000001.2777790.7296270.9444450.374079138.629620133.333340147.555560135.000000-15.88888926.777779-10.888889147.5555600.096352-2.21461252